Begin by examining the data set. Recognize how the data is recorded and how you may be able to use the given data to explore potential relationships between categories.
Scatterplot Questions
1. Create a scatterplot using categories that you feel may influence fuel efficiency. Answer the following questions.
Identify the two categories you chose and why you thought there might be a relationship between the two BEFORE creating the scatterplot?
Answer:The two categories we chose are Avg. MPG to Horsepower.We thought there was a relationship based on the more power the less mpg the vehicle used.(Common sense).
Create the scatterplot. Which category is your x-axis and which is your y-axis? Why did you create your scatterplot in that order?
Answer:x axis is avg mpg. and y is # of horses. The avg. mpg is dependent upon the independent horsepower.
Do you believe there is a relationship between the two categories? Why or why not?
Answer:Absolutely, there is a correlation. as the avg. mpg goes down the number of horsepower goes up.
If there appears to be a relationship, does it have a positive or negative slope? What does this mean about the relationship between the two categories?
Answer:negative slope, (see above)
Regression Questions
Create the linear regession equation in Excel. Include both the equation and the r2 value on the graph. Answer the following questions.
What is your regression equation? Explain what the equation means in relation to the categories.
Answer:y=-.0676x+35.448
What is your r2 value? Is this a strong correlation? Why or Why not?
Answer:.4301 There is a correlation, but not as accurate or as much of an indicator as could be.
Based on all the information you have, can you make any conclusions about your two categories? If so, what conclusions can you make? If not, why not?
Answer:Yes, you can draw conclusions. As the horsepower goes up the avg. mpg goes down. There is a correlation, but not as strong as it could be.
Analysis
Right click on the regression equation and select "Format Trendline". Explore the different variations of regression equations.
How would you determine which equation had the best relationship?
Answer:We based it on the R value.
Was the "Linear" option the optimal option? If so, why? If not, what was the better equation and why?
Answer:Not based on our reasoning for choosing a best type of line. The polynomial was the best.
Attach your Scatter Plots and Regression Information. Make sure your X and Y axis are correctly labeled. You may use Screen Shots to do so.
Begin by examining the data set. Recognize how the data is recorded and how you may be able to use the given data to explore potential relationships between categories.
Scatterplot Questions
1. Create a scatterplot using categories that you feel may influence fuel efficiency. Answer the following questions.
Answer:The two categories we chose are Avg. MPG to Horsepower.We thought there was a relationship based on the more power the less mpg the vehicle used.(Common sense).Identify the two categories you chose and why you thought there might be a relationship between the two BEFORE creating the scatterplot?
Answer:x axis is avg mpg. and y is # of horses. The avg. mpg is dependent upon the independent horsepower.Create the scatterplot. Which category is your x-axis and which is your y-axis? Why did you create your scatterplot in that order?
Answer:Absolutely, there is a correlation. as the avg. mpg goes down the number of horsepower goes up.Do you believe there is a relationship between the two categories? Why or why not?
Answer:negative slope, (see above)If there appears to be a relationship, does it have a positive or negative slope? What does this mean about the relationship between the two categories?
Regression Questions
Create the linear regession equation in Excel. Include both the equation and the r2 value on the graph. Answer the following questions.
Answer:y=-.0676x+35.448What is your regression equation? Explain what the equation means in relation to the categories.
Answer:.4301 There is a correlation, but not as accurate or as much of an indicator as could be.What is your r2 value? Is this a strong correlation? Why or Why not?
Answer:Yes, you can draw conclusions. As the horsepower goes up the avg. mpg goes down. There is a correlation, but not as strong as it could be.Based on all the information you have, can you make any conclusions about your two categories? If so, what conclusions can you make? If not, why not?
Analysis
Right click on the regression equation and select "Format Trendline". Explore the different variations of regression equations.
Answer:We based it on the R value.How would you determine which equation had the best relationship?
Answer:Not based on our reasoning for choosing a best type of line. The polynomial was the best.Was the "Linear" option the optimal option? If so, why? If not, what was the better equation and why?
Attach your Scatter Plots and Regression Information. Make sure your X and Y axis are correctly labeled. You may use Screen Shots to do so.